For example, in order to capture long-range spatial dependencies Berger et al.  [1]} propose computing multiple Gram matrices using translated feature tensors (so that the outer product is taken between feature vectors at fixed spatial offsets). While style losses based on Gram matrices can be seen related to capturing the second order statistics of features (the covariance matrix is proportional to the gram matrix after data is zero-centered), there have been efforts to use more complex parameterizations of the style features' distribution. For example Risser et al.  [2]} propose matching the marginal distribution of features using 1d histograms, and our own work (described in Chapter ) proposed matching the distribution of features using optimal transport.
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